Preface, September 17, 2005. Most of the
text that follows was written in 2002 and 2003, before I purchased my
first digital SLR (the Canon EOS-10D in March 2003; since replaced by
the EOS-20D and many more). Since July 2003 I've been intensively involved in
developing the Imatestprogram
for measuring image quality in cameras, lenses, scanners and printers.
I haven't been able to keep these pages up-to-date.

In Digital cameras vs. film,
parts
1 and 2, we use the tools developed in earlier in the
series
to compare digital and film cameras, and we address the question, "How
many pixels does a digital sensor need to outperform 35mm film?"
The answer is less speculative than it used to be: The 11+ megapixel
Canon
EOS-1Ds, EOS-1Ds Mark II, and EOS 5D
clearly outperform 35mm. I can make finer prints with the 8.3 megapixel
EOS 20D (razor sharp at 13x19 inches) than I ever could with 35mm— and
I was fanatic about lenses and darkroom work. We also look at the rapid
advances of
digital sensor technology, which have made some digital cameras
obsolete
in a matter of months. The good news is that the advances are slowing
down--
digital cameras are stabilizing and it has become safe to buy one
without
fear of rapid obsolescence (though
obsolescence
will still happen; just slower).

Part 1 describes the four pillars
of
image quality, digital image sensors, the simulation technique, and
presents
a summary of results comparing digital camera resolution with film. Part
2 contains Dennis Wilkins' comparison of the Nikon D100 with
film,
my view of the future of digital cameras, Links, a discussion of
Information
theory and image quality, and how to measure MTF from Dpreview.com test
results. I can't keep up with all the latest camera models. Sites with
the latest news and reviews are listed in Digital
cameras: Links. Digital camera sharpness measurements are
available on Imatest sharpness
comparisons.

If you are unfamiliar with MTF, you may want to review Part
1 of this series.

Green
is for
geeks. Do you get excited by an elegant equation? Were
you
passionate about your college math classes? Then you're probably a math
geek-- a member of a maligned and misunderstood but highly elite
fellowship.
The text in green is for you. If you're normal or mathematically
challenged,
you may skip these sections. You'll never know what you missed.

September
2004 I've released Imatest: a program for testing
digital image sharpness and quality using simple widely available
targets.

February
2004 I've
updated the simulations to reflect my experience with the Canon
EOS 10D. Digital cameras come off slightly better in the
comparisions.
The 10D has about 80% of the resolution of 35mm film in both the
simulations
and my tests. But
resolution
isn't the only factor that contributes to image quality. Noise (the
counterpart
of grain in digital cameras) is nearly absent in the 10D.

Thanks
to Miles Hecker
for his valuable
critiques
and guidance.Thanks
to Alex Tutubalin
for pointing out
some
errors-- painful, but this page is better as a result.

Introduction

I
became interested in the question, "How many pixels does it take for a
digital sensor to outperform 35mm film?," after I read a rave
review of the 3.1 megapixel Canon
EOS-D30, written in late 2000 by Michael Reichmann of Luminous-Landscape.com.
At $3,000, the D30 was the first "affordable" digital SLR
(DSLR),
accepting Canon's line of EF lenses. It was followed by the $2,200 6.3-megapixel
EOS-D60 in February 2002, the $1,500 6.3-megapixel EOS
10D in March 2003, and the $1,500 8.2-megapixel EOS-20D
in September 2004.

In September 2002 two full-frame digital
cameras
were announced: The 11 Megapixel Canon
EOS-1Ds and the 14 Megapixel Kodak
DCS Pro 14n. These cameras outperform
35mm film, winning the race as much on low noise (absence of grain--
far
superior to 35mm) as resolution. The Canon EOS-1Ds Mark II, announced in
September 2004, challenges medium format film, and the Canon EOS 5D,
announced in August 2005 drops the price of a full-frame 12.8 megapixel
SLR to $3,300 USD, not much more than the old 3 megapixel EOS-D30.

This is exciting news because of the many advantages of digital
cameras:
First of all there is no film, which is the primary factor limiting
resolution
in the 35mm format. There is very little noise (the digital
counterpart
of grain) in sensors with pixel spacings larger than about 6 microns.
There
are no problems with film flatness, inconsistent development, or
scanners.
You can preview your image immediately-- no trips to the camera shop to
drop off film and pick it up, and no worry about airport
x-rays.

The
four pillars of digital camera image quality-- an overview

Four major factors contribute to digital
camera
image quality.

Resolution,
by which we mean apparent sharpness and visible detail (closely related
to 50% MTF frequency), rather than the pixel count or traditional
lp/mm resolution limit. Resolution is the primary concern of this
page,
but it is only one of several quality factors. For 6 megapixel digital
SLRs-- the Canon EOS 10D, the Nikon D100, and the Fuji S2 Pro (which
actually
has 6 megapixels but uses sophisticated interpolation to simulate 12
megapixels)--
resolution is close, but not quite equal to 35mm film. But overall image quality can be stunning because color
quality
is excellent and noise-- the digital counterpart of grain-- is nearly
absent. For 11+
megapixel
digital SLRs, resolution is
superior.

Noise/grain
Noise in digital SLRs is much
lower than grain (its counterpart) in film. Digital SLRs exhibit almost
no visible noise at ISO 100 and 200, and still have low noise at 400
and
800. Compact digital cameras (with 11 mm diagonal or less sensors)
have fairly low noise at ISO 100, though noise can become quite
bothersome
at ISO 400 and above. Both resolution and noise (actually
signal-to-noise ratio)
enter into the equation for Shannon's information capacity, which I hypothesize is closely related to
perceived image quality.

Dynamic
range
Limitated dynamic range is the principal weakness of digital capture,
but
it is only a problem in contrasty light, and there are a number of
remedies.
(See, for example, the article on Tonal
quality and dynamic range in digital cameras.) It is most frequency
manifested as blown (burned out; flat white) highlights. Film responds
to light with a nice "S" curve--as light intensity increases, density
gradually
levels off in a region known as the response "knee," which is
responsible
for much of the detail and beauty in the highlights of fine
silver-based
prints. It is absent in digital sensors, but as long as highlights
aren't
blown out, prints from digital sources look fine. A "knee" can be
created
by means of Curves transformations
(illustrated here) in image
editors.
Help is on the way: Fuji's long-delayed SUPER
CCD SR technology (announced January 2003, but delayed until at
least
October) offers a solution using two sensors per pixel (one large, one
tiny).

Color
quality
We won't go into detail here, but in my experience digital is
consistently
outstanding-- better than film when prints are the end product (though
not comparable for slides). The reason is that there is less generation
loss. With a digital camera, the steps are (1) digital capture,
involving
filtered sensors, (2) Bayer interpolation (except for Foveon),
which doesn't affect color quality, (3) Image editing (no generation
loss
either, but lots of potential gains), and (4) Printing using pigments
or
dyes. With film the steps are (1) Exposure, using layered film, where
dye-couplers
determine the sensitivity of the layers, and (2) development, where the
dye couplers determine the color (light absorption) of each
layer.
A lot of sophisticated work has gone into making the light sensitivity
comparable to the spectral absorption of each layer. If (1) and (2)
were
the whole story, as it is for slides, film would be ahead, but there's
more for prints. For traditional prints you have to (3) expose color
paper
through an enlarging lens and (4) develop it. This involves the same
compromises
as (1) and (2). For digital prints you have to go through a process
similar
to digital camera capture. Bottom line:
colors in prints captured on digital cameras are wonderful-- subtle or
vivid, as appropriate. They have a three-dimensional quality rarely
seen
in prints from film originals.

All
of these properties can be measured with Imatest,
a program that measures digital image sharpness and quality using
simple, widely available targets. Resolution (MTF) is measured by SFR with a target you
can print yourself. Noise and dynamic range is measured by Q-13
Stepchart. Color quality is measured by Colorcheck.

Digital
image sensors

The key geometrical specifications of digital sensors are sensor size,
pixel spacing, and the number of pixels (horizontal h, vertical
v,
and total hxv. These
specifications are discussed in detail in my page on Digital
cameras. Sensor and pixel size issued are discussed below.
.

Pixel
size, sensor size, pixel count, and image quality

Pixel count (8 Megapixels, wow!)
grabs our attention
in digital camera specifications. But it doesn't tell everything. Pixel
and sensor size matters.
It turns out there is an optimum range for pixel size and an advantage
to large (i.e., costly) sensors. For reference, sensor diagonal
measurements
are 43.3 mm for full frame 35mm film; up to 11 mm for compact digital
cameras,
and 22 mm and over for digital SLRs.

Small pixels have excellent resolution but suffer
from increased noise
(hence poorer signal-to-noise ratio, SNR), reduced exposure range
(fewer
f-stops), and reduced sensitivity (lower ISO speed). The reason is simple: they respond to fewer photons and can
hold
fewer electrons. These effects are most noticeable in compact
digital cameras, which have pixels smaller than 4 µm.
The exact relationship between noise and pixel size is difficult to
quantify
since there are several noise
mechanisms, each of which scales differently.

Large pixels have good SNR,
ISO
speed and exposure
range, but suffer from aliasing-- low
spatial
frequency artifacts that appear when the lens has significant response
above the Nyquist frequency: 1/(2*pixel
spacing). Aliasing typically manifests as Moiré patterns on
images
with high frequency repetitive patterns, such as window screens and
fabrics.
It can be reduced by anti-aliasing (low pass) filters, which
are
expensive and unavoidably reduce resolution. In Optics
for digital photography from Schneider
Optics, the author states that aliasing will be adequately
controlled
if the MTF of the lens + sensor at Nyquist is no more than about 10%.
Compact
digital cameras, which have pixels smaller than 4 µm,
don't need anti-aliasing filters: the lens is sufficient. This helps
control
cost.

Small sensors run into problems with lens diffraction,
which limits image resolution at small apertures-- starting around f/16
for the 35mm format. At large apertures-- f/4 and above-- resolution is
limited by aberrations. There is a resolution "sweet spot" between the
two limits, typically between f/5.6 and f/11 for good 35mm lenses. The
aperture at which a lens becomes diffraction-limited is proportional to
the format size: 22 mm diagonal sensors become diffraction-limited at
f/8
and 11 mm diagonal sensors become diffraction-limited at f/4-- the same
aperture where it becomes aberration-limited. There is little "sweet
spot;"
the total image resolution at optimum aperture is less than for larger
formats. Of course cameras with small sensors can be made very compact,
which is attractive to consumers.

Large sensors cost more. No getting around it. That's
the major reason
compact digital cameras are so popular. 11 mm diagonal sensors have
1/16
the area of a 35mm frame. The problem with large sensors is manufacturing
yield-- the percentage of sensors that work properly. Suppose an 11
mm sensor has a 90% yield (pretty good). A 44 mm sensor (35mm format; 16x
the area) with the same process would have a yield of 0.9016
= 18% (not so hot). Larger sensors tend to have larger pixels, which
helps
the yield.

Compact digital cameras have sensors with diagonal dimensions between 5
and 11 mm, and pixel pitches 3.4 µm or
less.
These cameras have acceptably low noise at low ISO speeds and the best
of them-- the "prosumer" models-- can make excellent 8˝x11
inch or larger prints, depending on pixel count . Thanks to noise and diffraction, overall image quality decreases for pixels smaller than 2 µm.

The optimum pixel size for high quality imaging seems to
be in the 6-9 µm
range. Larger pixels have problems with aliasing and can't take
advantage
of high quality lenses. Smaller pixels have more noise and less dynamic
range and sensitivity. Digital SLRs will stick with 6-9 µm
pixels and evolve towards larger sensors with more pixels. 24x36
mm sensors with 16+ megapixels (7.4µm
or less pixel spacing) have performance approaching medium format
(see The future of digital cameras),
but they won't come cheap for quite some time.

.Two technologies are used to manufacture digital sensors: CCD
(charge-coupled device: a sort of pixel bucket brigade) and CMOS
(complimentary
metal oxide semiconductor): a process widely used for computer chips.
CCD
dominated high quality sensor applications until the Canon EOS D30 was
introduced in 2000; CMOS was thought to have intrinsically higher
noise.
But CMOS has been advancing rapidly; it promises to deliver better
performance
(including very low noise) and lower power consumption (hence better
battery life) at lower cost (CMOS fabrication lines are more widespread
and
more functions can be integrated on the sensor chip). Canon has a nice
site devoted to their CMOS sensor technology.

There are two types of digital sensor: (1) those that use a
color
filter array (CFA): a filter grid that covers the sensor so that each
pixel
is sensitive to a single primary color (R, G or B), and (2) sensors
that
employ the new Foveon X3
technology, where each pixel is sensitive to all three primary colors.
Conventional sensors all use CFA's, but Foveon's new technology
promises
to outperform them if Foveon can deliver..Bayer
pattern sensors Conventional digital
sensors
have their pixels are arranged as shown on the right, in a pattern
known
as the Bayer mask (or
filter,
or mosaic, or CFA for Color Filter Array), with two green pixels for
each
red and blue pixel. This is appropriate because the eye is most
sensitive
to green.

With the Bayer mask sensor there is some resolution loss and side
effects--
mostly
Moiré fringing-- due to interpolation--
the process of filling in the data for the two missing colors at each
pixel
location. The red and blue pixels are spaced
twice
as far apart as the green pixels; hence their intrinsic resolution is
only
half
that of green, but sophisticated reconstruction algorithms recover most
of the lost resolution as long as some green is present. Interpolation
triples the file size (for a given bit depth). For example, in the EOS
10D, a 7 Megapixel RAW file (one pixel per color, bit depth=12,
losslessly
compressed) is transformed into a 18.9 Megabyte 24-bit file (8 bits per
color, which sacrifices some tonal detail) or an 37.8 Megabyte 48-bit
file
(16 bits per color; which keeps the full tonal detail). (It's always
better
to edit in 48 bits, but
it's
OK to store the file in 24 bits once editing is complete.)

The best
interpolation
algorithms are iterative-- they are extremely computationally
intensive;
hence they are best performed on RAW image files in a computer.
In-camera
interpolation algorithms are less effective and more subject to
artifacts
such as Moiré.

Foveon's
X3 sensors Announced
in February 2002, represents a real breakthrough in digital sensor
technology.
DPreview.com
has a description
of the technology and full
review of the 3.4 megapixel Sigma
SD9 SLR. The X3 senses all three colors at each pixel position. No
Bayer interpolation is needed. It apparently doesn't need an
anti-aliasing
filter because its monochrome aliasing (Moiré fringing) is far
less
visible than the colored aliasing in Bayer sensors. Its MTF is very
high
around the Nyquist frequency-- enough to cause disaster with Bayer
sensors.
Using MTF as a measure of resolution, it meets the claim of doubling
image
resolution.
Mike
Chaney, author of Qimage
Pro, has performed a simulation
comparing a prototype Foveon sensor with a Bayer sensor by "Bayerizing"
the Foveon pixels in a Foveon image (filtering them red, green, or
blue),
then reconstructing the image using a realistic algorithm. He did this
without and with anti-aliasing filters. His results confirm a 25% gain
for the black and white test pattern, but the gap between Foveon and
Bayer
sensors is greater for patterns that lack green. Patterns were red and
blue dominate look worse with Bayer sensors. Foveon has a nice
paper on the MTF of its sensors.

The 3.4 megapixel Sigma
SD9 SLR camera body, reviewed by dpreview.com
and photo.net,
only takes
Sigma lenses. It faces tough competition from 6 megapixel Canon and
Nikon
bodies, that many photographers can use with their existing lenses. A
tough
sell but a promising future. [To learn
more
about the technology, check out US
patent 5,965,875-- not easy reading.]

The Fujifilm
S2 Pro is a Nikon-mount DSLR with a 6.17 megapixel "Super CCD"
that produces up to 12.1 Million (4256x2848) interpolated pixels. Price
is $1996
US. (Jauary 2004) I'm not sure how to simulate it. DPReview.com
has the URLs of two Japanese sites with sample images: Miscall
and Yamada
Kumio. It's well worth comparing the ISO 100 images from the EOS
D60, D100,
and S2
Pro on Kumio's site. Note particularly the block wall just below
the
clock. The S2 Pro is clearly superior; the S2 images on both sites are
amazing. Imaging-resouce
and Dpreview.com
have published very positive reviews; resoluton is clearly superior the
the D60 and D100. The S2 Pro may well equal 35mm.

The S2 Pro uses interpolation trickery to double the
number of pixels.
The idea is that the eye is more sensitive to horizontal and vertical
detail
than to diagonal detail. Vertical and horizontal detail also tends to
be
more prevalent in scenes. By arranging pixels diagonally, the
horizontal
and vertical spacing is reduced by a factor of the square root of 2
(0.7071x).
Interpolation takes place not only at pixel locations, but between
them,
doubling the number of pixels and improving the interpolated vertical
and
horizontal resolution. The octagonal pixels are said to be a better
match
for the microlens used to increase the sensor's effective fill factor.

Simulation
technique

We use the MTFcurve2 program to
evaluate
the performance of current and future cameras. MTFcurve2
calculates the spatial frequency response (the MTF) of each component
(lens,
film, sensor, etc.), and calculates the total system MTF.

The key parameter required to model a digital camera is the sensor's
MTF response. Solid data on sensor MTF is extremely hard to
find
on the Web. This forces us to make assumptions-- to choose models that
match test results. As we mentioned in Part 2,
the response of scanners and sensors can be approximated by functions
of
the form |sinc(f/dscan)|n, where dscan
is sensor resolution in pixels/mm.

These functions are simple, have
nulls
at the correct frequencies, and closely approximate measured data.
Their
frequency and spatial response is shown in graphs
in Part 2 for n = 1 through 4. The spatial sensitivity function for
sinc(f/dscan) is a rectangle of length 1/dscan (the length of a sensor
element with a fill factor of 1). An "ideal" rectangular sensor with a
100% fill factor would have a sinc(f/dscan) response. For n > 1, the
spatial
sensitivity is the rectangle convolved with itself (n-1) times. For sinc2
it is a triangle of length 2/dscan. As n increases, spatial response
spreads.

We select values of n for |sinc(f/dscan)|n
that yield results similar to available data. Diagnostics
for Digital Capture using MTF by Don Williams of Eastman Kodak
contains
an MTF plot for three digital cameras (Figure 2), one with evident
sharpening.
MTF for digital cameras tested by www.dpreview.com
can be estimated with the procedure outlined below.

The MTF of CCD digital sensors is affected by
diffusion or "blooming" between pixels. Most digital sensors have anti-aliasing
(lowpass) filters, which spread spatial response and reduce high
frequency
MTF. Bayer sensors have some MTF loss due to their layout and
interpolation
routines, though sophisticated software keeps this loss to a minumum.
Hence
no digital camera sensors would be expected to match the sinc(f/dscan)
response of an ideal rectangular sensor. After some trial-and-error the
following approximations were selected. [I changed the
assumptions
February 2, 2004 to conform to my experience with the Canon EOS 10D.
Digital
cameras come out better as a result.]

The MTF of
Bayer sensors
with anti-aliasing filters approximates |sinc(f/dscan)|3.
Anti-aliasing
filters are found in digital SLRs (except for the Kodak DCS Pro 14n). A
page on a low
cost Kodak anti-aliasing filter illustrates MTF response, which may
or may not resemble actual filters used in digital SLRs.

The MTF of
Bayer sensors
without anti-aliasing approximates |sinc(f/dscan)|2. These
include
compact digital cameras (with pixel size <= 4 µm) and the
Kodak
DCS Pro 14n, which has a relatively large 7.9 µm pixel
spacing--risky:
it could have a problem with Moiré fringing.

The MTF of a Foveon X3 sensor, which doesn't
require interpolation, and apparently doesn't have an
anti-aliasing
filter, approximates |sinc(f/dscan)|1.5. The X3 sensor
requires
less anti-aliasing than Bayer sensors because aliasing is black and
white
instead of color, hence much less obvious. It works better in
situations
where green energy is absent.

Since digital cameras have varying amounts of sharpening, which
strongly
affects the 50% MTF frequency, we apply consistent sharpening to the
simulations.
We compare digital camera resolution with Provia
filmscanned at 4000 dpi and sharpened.
This provides a fair comparison because it contains nearly all the
information
on the film-- a sharpened 4000 dpi scan results in a sharper
image than you can get through an enlarging lens.
.

The key result
is the black line: the MTF of the lens + sensor + sharpening.

The thin magenta
curve is the spatial response of the lens only. The kinky red
curve is the spatial response of the sharpened digital image. The thin blue
line is the MTF of the lens. The blue
dashed line is the MTF of the sensor + sharpening.

Since the 10D's sensor dimensions are 15.1/24 = 0.63 as large
as 35mm,
the total resolution at the 50% level is (61/46.7) * (15.1/24) = 0.82
that
of 35mm 4000 dpi Provia. That's fairly close, and when you consider the
extremely low noise of the digital sensor-- the absence of grain (see
the
discussion on information theory),
overall
image quality approaches that of 35mm. Another advantage of the low
noise
is that you can get away with more sharpening.

The EOS 10D has 82%
the total
resolution of full frame 35mm Provia scanned at 4000 dpi.But perceived image quality approaches
that of film because of the low noise-- the absence grain.

Simulations of the Foveon X3 sensor assuming a
|sinc(f/dscan)|1.5
response indicates that a 105 pixel/mm sensor would have the same 50%
MTF
frequency as the 135 pixel/mm EOS 10D. On this basis I estimate that
the
linear resolution of the Foveon X3 in a camera is about 25 to
28%
higher than a conventional sensor with the same pixel spacing. Based on
resolution only, the X3 sensor would have the same image quality as a
conventional
sensor with approximately 1.6x as
many
pixels. But since the X3 has greatly reduced artifacts (Moiré,
etc.),
I'd guess the advantage would be about 1.8x,
i.e., the 3.4 megapixel Foveon X3 sensor in the Sigma
SD9/SD10 would equal a conventional Bayer sensor with 6.1
megapixels--
almost equal to the Canon EOS 10D and Nikon D100. (I suspect that the
severe
Moiré in the photo.net
SD9 preview is at least partially the result of an inferior Bayer
interpolation
routine.)

Digital
camera resolution summary

Here is where we take
advantage of the predictive power
of the MTFcurve2 program. The
tables
below summarize the simulated resolution of several digital cameras.
Because of their low noise, which allows greater sharpening, many
people
will find digital cameras to be sharper than the numbers indicated
here.
The table below is not
comprehensive. I omitted cameras like the Fuji
S2 Pro, whose interpolated Super CCD
response
is difficult to simulate.

The
measuredresolution
of several digital cameras can be found in Sharpness comparisons
on the Imatest
website. Imatest
is a software package that allows you to measure lens sharpness and
digital camera performance using inexpensive easily-available
targets. The comparisons between cameras are similar to those
presented below, but the absolute values are different because a
sharpening radius of 2 (vs. 1 here) is used in the analysis.

3A.
Film (Provia
100F scanned at 4000 dpi and sharpened, which results in sharper prints
than conventional enlargements) has about the same spatial resolution
as
anti-aliased Bayer sensors with 10.2 micron pixels.

4.
Resolution relative to 35mm is calculated by the equation, (50%
MTF)/46.7
* (sensor height)/24. This is linear (not total, or
area)
resolution.

6.
You can get about
20% more resolution form a perfect film image with an 8000
dpi drum scan, but I chose 4000 dpi for the benchmark because few
photographers
can afford 8000 dpi scans on a regular basis.

7.
Lenses for compact
digital cameras (11mm or smaller diagonal sensors) should be sharper
(have
more extended MTF response) than 35mm lenses because they cover a much
smaller area and tend to have optimum sharpness at larger apertures,
around
f/4 - 5.6. But MTF data for these lenses is unavailable, so I based the
resolution
relative to 35mm on the "excellent 35mm lens."
This
is pessimistic; the actual number is undoubtedly higher. I won't
simulate
the Sony DSC-F828 until I have good lens data. Measured results are on
Imatest Sharpness
comparisons.

Based
on the equivalence of 10.2 micron pixels with film, I estimate that a
full-frame sensor with 8.3 megapixels would have resolution equal to
35mm
film. Slightly more pixels would be required for smaller
sensors with lenses designed to cover full-frame 35mm. The simulated
resolution
of the Canon EOS 10D relative to 35mm film (82%) agrees well with my
tests on the 10D. Likewise, the simulated resolution of the Nikon
D100
agrees with Dennis Wilkins' tests.

The image on the right was cropped from a 3072x2048
pixel image taken with the EOS 10D. Details can be found on the EOS
10D page. It illustrates how digital cameras make highly efficient
use of pixels; images tend to be sharper at the pixel level than 35mm
scans.
There are no MTF losses from film, film flatness error, development, or
scanning (optical and mechanical). Sharpness is consistent
and there is little noise/grain. That's why digital camera images
achieve
comparable perceived image quality with fewer pixels.

In interpreting these results, remember that resolution
is not the only factor that influences image quality. Digital cameras
with
large pixels (over 5 µm) have far less noise (the equivalent of
grain)
than film, hence they will have better image quality
with
the same resolution. I find the image quality in my 6.3 megapixel
Canon
EOS 10D to be equal to 35mm film-- remarkable when you consider
that
35mm film exposed and processed with excellent technique (fine lens
around
optimum aperture, perfect focus, sturdy tripod) can produce remarkably
sharp 13x19 inch prints-- finer
than
most people realize. I haven't used much film (except for the Hasselblad
XPan) since I got it.

Michael
Reichmann's
article, Ultimate
Shoot-out, compares his 11 Megapixel EOS-1Ds to the medium format
Pentax
67II. Although I agree with his conclusion that the EOS-1Ds has better
overall image quality-- I'd buy one today if I could comfortably afford
it-- I have to dispute one important detail: which camera has the
higher
resoluton. It's the Pentax. You can see it on the enlargements of the
windows
in the middle of the page, the second set of images under
The Print
Evaluation. A vertical line on the wall on the right is visible
in the Pentax image but not in the EOS-1Ds image. Should this change
Michael's
essential conculsions? No. This thin line represents response at a very
high spatial frequency that would have little effect on the appearance
of a 13x19 inch image. It would be
visible on a 24x30 inch image, but
the 1Ds still has superior image quality due to the absence of grain--
see the discussion on Shannon channel
capacity
and image quality. Sharpness is only a part of the equation.
Reichmann's
earlier
review
of the
D60, with side-by-side images from the D30, D60, 35mm and medium
format
cameras, is also worth reading.

The
Foveon X3 sensor is an important step above
the traditional Bayer mask sensor; images will look better at the same
pixel count because of reduced artifacts (Moiré, etc.). But
Foveon
needs to increase the pixel count of its sensors to compete
successfully.